In a modern warfare environment many moving vehicles (airplanes, ships, tanks, etc.) are now equipped to include electronic warfare (EW) systems which assist in detecting, identifying and targeting opposing forces. One function of an EW system may be to detect and identify radar signals emitted from a number of different systems. This can be a very complicated task depending on the number of emitters within a particular environment.
In order to provide for this emitter identification one function performed by EW systems is the deinterleaving of the radar signals from the various emitters. Deinterleaving typically includes sorting of received pulses according to an analysis of various parametric information. Modern radar applications that perform deinterleaving may include those used in systems such as Electronic Warfare Support Measures (ESM) and ELectronic INTelligence (ELINT) radar systems. Deinterleaving radar pulses includes detecting and recognizing different simultaneously active radar emitters.
In a basic ELINT collection system, front end electronics are the first elements to receive RF energy from the signal environment. The front end electronics are composed of antenna(s) and signal detection components and may contain filters and amplifiers that improve the detection capabilities of the sensors. Digitized signals from the front end electronics are fed to a parameter measurement unit (PMU), which generates a parametric description of the signal. A parametric description includes characteristics like the pulse's time of arrival (TOA), amplitude, pulse width, frequency and electrical phase angle, for example. The digitized pulse trains are then fed to a deinterleaver. A conventional deinterleaver is apparatus that receives a plurality of interleaved pulse trains and deinterleaves a composite data stream into constituent pulse trains by detecting patterns in samples of pulses.
Deinterleavers typically identify the individual emitters that transmit each separate pulse train, where parameters, such as a “pulse repetition interval” (PRI) in a transmitted data stream, are analyzed. PRI determination, for example, allows pulses of a given radar to be separated from a background of pulses for radar identification. A PRI is typically determined by processing the time history of data corresponding to a discrete emitter. Each radar may be characterized by one or more patterns of PRIs that repeat from a given start time.
A large volume of data is required to be processed by a system such as an ELINT radar system. It follows that weight, size, cost, complexity, processing power, power consumption, and method of execution of the processing of the data are critical to improving system performance. This is especially important for allowing a single air vehicle, such as an unmanned air vehicle or “UAV”, or missiles for example, to incorporate additional and improved capabilities.
In general, a sequence search algorithm identifies sequences of pulses where PRIs and phases of pulses in a single or multi-collector bitstream are extracted. For a PRI of m sequence intervals, there are m possible phases and N (sampling intervals) divided by m TOA's to be correlated. When the PRI of data streams is assumed to be from 1 to N, then a search of all possible sequences requires the order of N×N/m×m=N2 computations. When additional processing is used to increase accuracy and resolution, the order can increase to N3 or more.
Traditional deinterleavers may also perform repeated nested searches through PDW data, so that an amount of processing work involved in deinterleaving becomes proportional to the square or the cube of the number of PDWs to be processed. Thus, there is a need for reducing the processing work required for throughput of PDW data. The conventional deinterleaving systems may partition the data, for example, in groups that are defined in terms of one or more parameters. When parametric binning is performed it introduces the probability of signal fragmentation for parametrically agile signals.
In general, the computation load for applications such as ELINT are growing faster than Moore's Law can compensate, because data stream densities are increasing rapidly. Techniques such as Low Probability of Intercept (LPI) require higher sensitivity, thus intercepting larger volumes of data. In addition, data becomes lost when there is insufficient bandwidth to move all data from air, space, and ground collectors to ground processing. On-board fault management for conventional general purpose computers (GPs) is problematic for certain types of systems. There is also a demand for networked threat capability and short on time operating regimes, which are each rendered extremely expensive by using conventional systems that increase in complexity with a throughput density amount. Existing approaches require large SWaP support environments and are unable to be used in, for example, UAVs.
Conventional systems typically use a priori knowledge, which is further complicated by the need for comparing data in an environment that can include signals utilizing multilevel stagger and jitter. Since comparing in an a priori system is often backwards in time, a truly parallel algorithm cannot be implemented. What is needed is a method of evaluating potential relationships (i.e., scoring) that can be truly performed in parallel.
Embedded platforms require the technical ability to meet increasing size, weight, and power (SWaP) requirements of an operational unit, such as in an airborne system. These platforms also need a level of autonomy to handle faults and differing remote operations, such as with networking. Traditional systems use embedded computers, high-speed networks, and high performance middleware to provide integrated platforms, but these conventional methods of airborne computing have detrimental effects on associated ground support systems, such as a requirement that characteristics of individual applications be tailored or that significant system margins be included, in order to provide a necessary throughput and resulting quality.